Model-Based Decisions in Sepsis

描述(由申请人提供)：针对脓毒症等急性炎症性疾病的免疫调节干预的大型随机临床试验的记录令人沮丧。宿主-病原体相互作用的生物学复杂性，以及成功治疗对医疗保健系统和社会的潜在巨大影响，使脓毒症等疾病成为FDA关键路径文件和NIH路线图倡议中提出的基于模型的治疗方法的理想试验台。然而，目前还缺乏生物体水平的炎症计算模型。然而，更根本的是缺乏可以验证此类模型的人类数据集。这种数据集的组装将非常昂贵，而且在没有证明有效的模型的情况下，仅为测试基于模型的干预措施而获得的数据集极不可能。由美国国立卫生研究院资助的早期败血症休克原型护理(PROCESS)研究目前正在一项1350名患者的前瞻性随机试验中检验早期复苏对严重脓毒症患者的影响，并将产生一个粒度数据集，该数据集不仅将有助于了解脓毒症所涉及的过程，还将有助于了解生理学目标导向治疗方案的生物学后果。该提案中概述的计划的首要目标是通过先进的数学和计算方法，使用来自过程研究的数据来验证人类脓毒症的计算模型。我们已经组建了一个由模型师和临床医生组成的跨学科小组，他们在开发、校准和测试不同粒度的急性炎症，特别是脓毒症的电子模型方面有着雄辩的合作记录。我们认为，在大量临床相关的队列中验证计算机模型对于将计算建模作为一项技术合法化是绝对关键的，这项技术将被证明是设计更智能的随机干预试验的关键，特别是个性化治疗。一方面，利用过程试验的数据和初步分析，另一方面，利用现有的跨学科努力来扩展急性炎症反应的现有计算模型，也将提供一个前所未有的机会，从机制上了解导致器官衰竭和死亡、全身恢复和意外衰竭的过程。

DESCRIPTION (provided by applicant): Large randomized clinical trials of immunomodulatory interventions for acute inflammatory diseases such as sepsis have had a dismal track record. The biological complexity of the host-pathogen interaction and the potential large impact of a successful treatment on the health care system and society position diseases such as sepsis as ideal test beds for model-based therapeutic approaches, as proposed in the FDA critical path document and the NIH roadmap initiative. Yet, there is a paucity of organism-level computational models of inflammation. More fundamental however, is the lack of human data sets where such models could be validated. Such a data set would be extraordinarily expensive to assemble and is highly unlikely to be acquired merely for testing model-based interventions in the absence of models with demonstrated validity. The NIH-funded Protocolized Care for Early Septic Shock (ProCESS) study is currently examining the impact of early resuscitation in victims of severe sepsis in a 1350 patient prospective randomized trial and will produce a data set with a granularity that will not only help to understand the processes involved in sepsis, but also the biological consequences of a physiologic goal-directed treatment protocol. The overarching goal of the program outlined in this proposal is to validate computational models of human sepsis using data from the ProCESS study through advanced mathematical and computational methods. We have assembled a transdisciplinary group of modelers and clinicians with an eloquent track record of successful collaboration on developing, calibrating and testing in silico models of acute inflammation, and of sepsis in particular, of different levels of granularity. We believe that validation of in silico models in a large clinically relevant cohort is absolutely cruial to the legitimization of computational modeling as a technology that will prove pivotal to the design of smarter randomized interventional trials in general, and of personalized therapies in particular. Leveraging data and preliminary analyses from the ProCESS trial on the one hand and an extensive existing transdisciplinary effort at expanding existing computational models of the acute inflammatory response on the other will also provide an unprecedented opportunity to gain mechanistic understanding of the processes leading to organ failure and death, systemic recovery and unexpected failure.